Tumor-derived cell lines have served as vital models to advance our understanding of oncogene function and therapeutic responses. Although substantial effort has been made to define the genomic constitution of cancer cell line panels, the transcriptome remains understudied. Here we describe RNA sequencing and single-nucleotide polymorphism (SNP) array analysis of 675 human cancer cell lines. We report comprehensive analyses of transcriptome features including gene expression, mutations, gene fusions and expression of non-human sequences. Of the 2,200 gene fusions catalogued, 1,435 consist of genes not previously found in fusions, providing many leads for further investigation. We combine multiple genome and transcriptome features in a pathway-based approach to enhance prediction of response to targeted therapeutics. Our results provide a valuable resource for studies that use cancer cell lines.

Dark-grown seedlings exhibit skotomorphogenic development. Genetic and molecular evidence indicates that a quartet of Arabidopsis Phytochrome (phy)-Interacting bHLH Factors (PIF1, 3, 4, and 5) are critically necessary to maintaining this developmental state and that light activation of phy induces a switch to photomorphogenic development by inducing rapid degradation of the PIFs. Here, using integrated ChIP–seq and RNA–seq analyses, we have identified genes that are direct targets of PIF3 transcriptional regulation, exerted by sequence-specific binding to G-box (CACGTG) or PBE-box (CACATG) motifs in the target promoters genome-wide. In addition, expression analysis of selected genes in this set, in all triple pif-mutant combinations, provides evidence that the PIF quartet members collaborate to generate an expression pattern that is the product of a mosaic of differential transcriptional responsiveness of individual genes to the different PIFs and of differential regulatory activity of individual PIFs toward the different genes. Together with prior evidence that all four PIFs can bind to G-boxes, the data suggest that this collective activity may be exerted via shared occupancy of binding sites in target promoters.

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Glucocorticoids play important roles in the regulation of distinct aspects of adipocyte biology. Excess glucocorticoids in adipocytes are associated with metabolic disorders, including central obesity, insulin resistance and dyslipidemia. To understand the mechanisms underlying the glucocorticoid action in adipocytes, we used chromatin immunoprecipitation sequencing to isolate genome-wide glucocorticoid receptor (GR) binding regions (GBRs) in 3T3-L1 adipocytes. Furthermore, gene expression analyses were used to identify genes that were regulated by glucocorticoids. Overall, 274 glucocorticoid-regulated genes contain or locate nearby GBR. We found that many GBRs were located in or nearby genes involved in triglyceride (TG) synthesis (Scd-1, 2, 3, GPAT3, GPAT4, Agpat2, Lpin1), lipolysis (Lipe, Mgll), lipid transport (Cd36, Lrp-1, Vldlr, Slc27a2) and storage (S3-12). Gene expression analysis showed that except for Scd-3, the other 13 genes were induced in mouse inguinal fat upon 4-day glucocorticoid treatment. Reporter gene assays showed that except Agpat2, the other 12 glucocorticoid-regulated genes contain at least one GBR that can mediate hormone response. In agreement with the fact that glucocorticoids activated genes in both TG biosynthetic and lipolytic pathways, we confirmed that 4-day glucocorticoid treatment increased TG synthesis and lipolysis concomitantly in inguinal fat. Notably, we found that 9 of these 12 genes were induced in transgenic mice that have constant elevated plasma glucocorticoid levels. These results suggested that a similar mechanism was used to regulate TG homeostasis during chronic glucocorticoid treatment. In summary, our studies have identified molecular components in a glucocorticoid-controlled gene network involved in the regulation of TG homeostasis in adipocytes. Understanding the regulation of this gene network should provide important insight for future therapeutic developments for metabolic diseases.

Allele-specific gene expression, ASE, is an important aspect of gene regulation. We developed a novel method MBASED, meta-analysis based allele-specific expression detection for ASE detection using RNA-seq data that aggregates information across multiple single nucleotide variation loci to obtain a gene-level measure of ASE, even when prior phasing information is unavailable. MBASED is capable of one-sample and two-sample analyses and performs well in simulations. We applied MBASED to a panel of cancer cell lines and paired tumor-normal tissue samples, and observed extensive ASE in cancer, but not normal, samples, mainly driven by genomic copy number alterations.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-014-0405-3) contains supplementary material, which is available to authorized users.